An adaptive sampling scheme guided by BART—with an application to predict processor performance

DOI10.1002/cjs.10049zbMath1190.62143OpenAlexW1976009969MaRDI QIDQ3561488

Qingzhao Yu, Lu Peng, Zhide Fang, Bin Li

Publication date: 25 May 2010

Published in: Canadian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/cjs.10049

zbMATH Keywords

adaptive designBayesian additive regression treessequential \(G\)-optimization

Mathematics Subject Classification ID

Bayesian inference (62F15) Applications of statistics in engineering and industry; control charts (62P30) Sampling theory, sample surveys (62D05) Sequential statistical design (62L05) Reliability, testing and fault tolerance of networks and computer systems (68M15)

Related Items (3)

Model-guided adaptive sampling for Bayesian model selection ⋮ Model guided adaptive design and analysis in computer experiment ⋮ Sequential design for computer experiments with a flexible Bayesian additive model

Uses Software

• EGO
• BayesTree
• BartPy

Cites Work

• Greedy function approximation: A gradient boosting machine.
• BART: Bayesian additive regression trees
• Bayesian synthesis: combining subjective analyses, with an application to ozone data
• Efficient global optimization of expensive black-box functions
• The design and analysis of computer experiments.
• Active sampling for class probability estimation and ranking
• Bayesian Treed Gaussian Process Models With an Application to Computer Modeling
• A spatially-adjusted Bayesian additive regression tree model to merge two datasets
• Unnamed Item

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